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# install.packages('seriation')
library(seriation)
library(ggplot2)
data('Chameleon')

n_rows <- nrow(chameleon_ds4)
n_cols <- ncol(chameleon_ds4)
device_array <- eval.polyglot('grcuda', 'DeviceArray')

data <- device_array('double', n_rows, n_cols)
for (r in 1:n_rows) {
  for (c in 1:n_cols) {
    data[r, c] <- chameleon_ds4[r, c]
  }
}

labels <- device_array('int', n_rows)

dbscan_fit <- eval.polyglot('grcuda', 'ML::cumlDpDbscanFit')
eps <- 5
min_samples <- 15
max_bytes_per_batch <- 0
verbosity <- 0

dbscan_fit(data, n_rows, n_cols, eps, min_samples, labels, max_bytes_per_batch, verbosity)
chameleon_ds4$label <- labels[1:n_rows]

print(
  ggplot(chameleon_ds4, aes(x,y, color=factor(label))) + geom_point() +
    scale_colour_viridis_d(
      name='Cluster',
      labels=c('outlier', '0', '1', '2', '3', '4', '5')))
